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Found 8 papers, 4 papers with code
Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks
To handle the problem, we propose a ternary spike neuron to transmit information.
Membrane Potential Batch Normalization for Spiking Neural Networks
All these BNs are suggested to be used after the convolution layer as usually doing in CNNs.
RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networks
Spiking Neural Networks (SNNs) as one of the biology-inspired models have received much attention recently.
InfLoR-SNN: Reducing Information Loss for Spiking Neural Networks
The Spiking Neural Network (SNN) has attracted more and more attention recently.
Joint A-SNN: Joint Training of Artificial and Spiking Neural Networks via Self-Distillation and Weight Factorization
In this paper, we propose a joint training framework of ANN and SNN, in which the ANN can guide the SNN's optimization.
IM-Loss: Information Maximization Loss for Spiking Neural Networks
To deal with this problem, the Information maximization loss (IM-Loss) that aims at maximizing the information flow in the SNN is proposed in the paper.
Ranked #6 on
Event data classification
on CIFAR10-DVS
Real Spike: Learning Real-valued Spikes for Spiking Neural Networks
Motivated by this assumption, a training-inference decoupling method for SNNs named as Real Spike is proposed, which not only enjoys both unshared convolution kernels and binary spikes in inference-time but also maintains both shared convolution kernels and Real-valued Spikes during training.
RecDis-SNN: Rectifying Membrane Potential Distribution for Directly Training Spiking Neural Networks
Unfortunately, with the propagation of binary spikes, the distribution of membrane potential will shift, leading to degeneration, saturation, and gradient mismatch problems, which would be disadvantageous to the network optimization and convergence.
